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Integrating Local LLMs with Ollama and Python 🤖💻

Did you know that integrating local large language models (LLMs) into your Python projects can help improve privacy, reduce costs, and build offline-capable AI-powered apps? 👉

To get started, follow these steps:

• Set up Ollama and pull the models you want to use.
• Connect to them from Python using the <code>ollama</code> library.

Watch this video on how to integrate OLLAMA into your python projects https://youtu.be/E4l91XKQSgw?si=3gaeoM3EbvO6QYIC

Key Benefits:

• Improved privacy
• Reduced costs
• Offline-capable AI-powered apps

Uv vs Pip: Choosing the Right Package Manager for Your Python Projects 👉

When it comes to choosing a package manager for your Python projects, you have two popular options: uv and pip. While both tools share many similarities, there are key differences that may sway your decision.

* pip: Great for out-of-the-box availability, broad compatibility, and reliable ecosystem support. It's perfect for new projects or when you need to install popular packages quickly.
* uv: Worth considering if you prioritize fast installs, reproducible environments, and clean uninstall behavior. uv is ideal for streamline workflows for new projects, and its custom installation process can be beneficial for large-scale applications.

Here's a quick summary of the key differences:

* Package Installation: 📦
* pip: Easy to install popular packages using pip.
* uv: Requires manual package management, but can lead to faster installs and more reproducible environments.
* Dependency Management: 💻
* pip: Provides automatic dependency resolution for most projects.
* uv: Requires custom dependency management, which can be beneficial for complex projects.

By considering these factors and comparing the two tools, you'll make an informed decision that suits your specific needs.

🚀 Lightweight X11 App Launcher for Python 🎉

X11 app launchers are powerful tools that can greatly enhance your Python development experience. However, creating a custom launcher from scratch can be challenging and time-consuming.

Here's why:

*   Performance: Creating a native X11 application requires a deep understanding of C, X11, and the underlying operating system.
*   Portability: Building a cross-platform application can be difficult due to the varying differences between platforms.
*   Customizability: Custom launchers require a good grasp of the underlying architecture and configuration options.

On the other hand, you can use existing libraries like pywinauto or pyppeteer to create a lightweight X11 app launcher for Python.

In this example, we'll create a simple launcher using pywinauto, which provides an easy-to-use API for automating desktop applications.

### Example Code

import pywinauto

def launch_app():
    # Create the main window
    app = pywinauto.application().start('MyApp')

    # Launch the main menu
    menu = app.top_menu()
    menu.show()

# Start the application
launch_app()

### Features

*   Native X11 Application: This launcher uses the native X11 API to create a seamless desktop experience.
*   Easy-to-Use Interface: The pywinauto library provides an intuitive API, making it easy to customize and extend the launcher.

### Benefits

*   High Performance: The native X11 application ensures optimal performance and responsiveness.
*   Cross-Platform Compatibility: This launcher is designed to be cross-platform, making it suitable for deployment on various operating systems.

By using pywinauto to create a lightweight X11 app launcher for Python, you can take advantage of the power of C and X11 while still enjoying a seamless desktop experience. Give this example a try and see how it can enhance your Python development workflow!

Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment.

I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion"

Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention.

High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments.

24/7 Operation: Ant AI continuously searches for customers and recommends products. You only need to monitor progress via your mobile phone, requiring no additional management time.

II. Your Profit Guarantee: Low Risk, High Transparency, Zero Inventory Pressure, Stable Commission Sharing

We have established partnerships with platforms such as Shopee and Amazon, which directly provide abundant product sourcing. You don't need to worry about inventory or logistics. After each successful order, the company will charge the merchant a commission and share all profits with you. Earnings are predictable and withdrawals are convenient. Member data shows that each bot can generate $30 to $100 in profit per day. Commission income can be withdrawn to your account at any time, and the settlement process is transparent and open.

Low Initial Investment Risk. Bot development and testing incur significant costs. While rental fees are required, in the early stages of the project, the company prioritizes market expansion and brand awareness over short-term profits.

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This project uses YOLOv8 for object detection, OpenCV for video processing, and custom-defined security zones to detect intrusions in restricted areas. When a person enters a defined zone, the system triggers an alert sound and automatically captures snapshots for evidence.


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When deploying and running Python code on cloud-based clusters using Ray, one specific technical detail to focus on is the use of ray.remote decorator.

When applying the @ray.remote decorator to your Python functions, ensure that each function takes a single argument (the object being acted upon) and returns a value. This allows Ray to properly serialize and de-serialize data for distributed computing. For example:

import ray

@ray.remote
def my_function(x):
    # do some computation on x
    return result

Source: https://towardsdatascience.com/ray-distributed-computing-for-all-part-2/

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